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UTM Scholars Among Research Teams Awarded DSI Catalyst Grants

Friday, February 25, 2022 - 1:42pm
Tanya Rohrmoser
Data Sciences Institute funds 17 cross-disciplinary teams to solve complex global problems

The University of Toronto’s Data Sciences Institute (DSI) recently announced that it is funding seventeen multidisciplinary research teams—and three include scholars from UTM. Offering grants valued at up to $100,000 for two years, the competitive program brings together Collaborative Research Teams focused on developing new data science methodology or using existing methodology in innovative ways.   

From addressing data justice to avoiding a quantum crisis, these researchers bring together diverse perspectives to pose creative solutions to pressing problems. Learn about their projects—and how they’re using data science to tackle some of the world’s most complex challenges.

 


 

Better Data Access to Provide More Complete Picture of Environmental Changes

UTM researchers Yuhong He (Geography, Geomatics & Environment) and Kent Moore (Chemical & Physical Science) received a Catalyst Grant for their project, 50 years of spatial-explicit environmental data to examine changes in northern Canada.

The unique collaboration brings together their respective backgrounds in remote sensing and climate change; they’re looking to gain a better understanding of changes occurring in Northern Canada—which is experiencing unprecedented changes to its climate and ecosystems.

Yuhong He
Yuhong He, GGE

The problem? Notable gaps exist in the coverage of key components and, though Earth observations (remotely sensed observations for the physical, chemical, and biological systems of the Earth) provide a way to understand these changes and their implications, the massive data that comes in is complex, inconsistent, and generated from a variety of sources—making it incredibly difficult for users to access and interpret.

“For many northern communities, reliable data that speaks to the impact of a shifting climate on regional ecosystems is difficult to access,” Moore explains, “it doesn’t exist in aggregate in a useable or scalable way—and that means it’s harder for policy makers to make informed decisions.”  

Their solution? By integrating Earth observations and climate data going back to the 1970s, He and Moore will build a series of up-to-date, standard, consistent spatial datasets—a reproducible management system that will enable researchers to upload, share, and download spatial and data spanning nearly 50 years.

Kent Moore
Kent Moore, CPS

Through its development of open access geospatial datasets, the project will not only enable researchers across disciplines to analyze environmental change in northern Canada, but it will establish a data management system for both sharing and using these datasets—and that, in turn, will inform approaches and allow users to better develop local climate change adaptation strategies.

“This project is the first of its kind,” shares He, who predicts the dataset will also build capacity for more robust analysis and collaboration. “It will facilitate unprecedented information sharing across different communities—and it speaks to how data science can address issues relating to sustainability as well as data justice.” 

 

Researchers Benchmark Quantum Computing Against Classical Algorithms

Grant recipient Ulrich Fekl (Chemical & Physical Sciences) is collaborating with U of T’s Hans-Arno Jacobsen (Electrical & Computer Engineering, Faculty of Applied Science & Engineering) on Preventing a Reproducibility Crisis in Quantum Computing.

Ulrich Fekl
Ulrich Fekl, CPS

If that sounds complicated, it is. Until now, characterizing the properties of materials can be done one of two ways: experimenting in a lab—limiting the number of compounds that can be considered; or computationally, by solving the Schrödinger equation—which is so complex that traditional computing methods are either inaccurate, too expensive, or limited in which molecules they work on.

Recently, however, special-purpose quantum computers and simulators have been developed; run on the right hardware, truly accurate and fast quantum mechanics could, for the first time, become feasible.

“In research, you never know where the next opportunity is coming from,” says Fekl of the newly funded project. “Everything that can ever be measured in an experiment can also be accurately predicted with pure computation, by solving a quantum mechanics equation.”

It’s ground-breaking stuff. And as players like Google and IBM rush in to plant their stakes in the ground, each one computes their own molecules, because an agreed-upon set of molecules to be used as a common standard has not yet emerged. Often, algorithms are proprietary. A common basis for comparison is needed—Fekl and Jacobsen anticipate a reproducibility crisis if a good basis for rigorous comparison is not found soon.

The U of T researchers are teaming up to benchmark quantum computing against classical algorithms for the purposes of predicting molecular properties, planning to openly release their datasets, algorithms, and methodologies. The excitement over these new technologies may be creating a ‘Wild West’ scenario, but they’re determined to tame it, committing to establishing the rigorous evaluation needed to test and challenge current—and future—developments in obtaining molecular data.  

“In the end, quantum computers will be only useful and trusted devices if both speed and accuracy are right,” Fekl says practically. “Our molecular testbench will grow with the growing capabilities of quantum computers and will eventually approach an infinite, 3D-extended material.”

 “This grant means a lot,” he remarks, noting the collaboration was brought together by discussions with fellow Canadian and international researchers interested in quantum computing, “and we anticipate that this is just the beginning.”

 

3D Model to Accelerate Development of Effective Cancer Treatments

Joshua Milstein (Chemical & Physical Sciences) is partnering with Alison McGuigan and Rodrigo Fernandez-Gonzalez—both of the Institute of Biomedical Engineering at U of T—on Bioimage Informatics for Exploring Heterogeneous Cell Communities and Accelerating the Development of Effective Cancer Treatments.

Joshua Milstein
Joshua Milstein, CPS

Researchers focusing on targeting cell populations that may be resistant to chemotherapy could soon benefit from this innovative project. Currently, most research labs study cancerous tumour cells within a single, thin layer—but that doesn’t give the full picture. Milstein’s team is employing a fully 3-dimensional tumour model that will allow researchers to get a clearer look at how therapy reacts to cells and how cell populations interact and regrow.

The project is co-funded by Medicine by Design, which fosters multidisciplinary collaborations that are finding solutions to challenges in regenerative medicines—projects in which data science is often fundamental. Medicine by Design is partnering with researchers on two of the Catalyst Grants. 

“Much of the research in my lab is focused on extracting detailed, quantitative information from microscopy images. We’ve spent years developing efficient algorithms to identify clusters of proteins and to accurately determine the abundance and interactions of these proteins within extremely large, single-molecule imaging datasets,” Milstein explains. “We hope to apply the skills and knowledge we have acquired in the field of single-molecule imaging to the altogether new field, for us, of high-throughput drug screening.”

As a result, therapy response analysis and screenings will be able to predict drug combinations that will better target cell populations, and that, in turn, will accelerate drug discovery. Additionally, state-of-the-art bioimage informatics tools will promote the adoption of a standardized image analysis pipeline and support more reproducible and reliable research.

“We’re hopeful that this will act as a seed project that promotes interactions between a much larger network of tissue engineering and image informatics experts at U of T,” notes Milstein on the collaboration. “When people begin to interact with researchers across disciplines, researchers with a diversity of skills and ideas, that’s often when the most progress is made in the sciences.”

 


 

The Data Sciences Institute Catalyst Grants are supported by the University of Toronto Institutional Strategic Initiatives and external funding partners. Want to learn more about the award recipients and their projects? To see the full list, read the DSI Catalyst Grants announcement.